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Power system short-term load forecasting based on neural network with artificial immune algorithm

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3 Author(s)
Huang Yue ; School of Information Science and Engineering, Shenyang Ligong University, 110168, China ; Li Dan ; Gao Liqun

This paper offers one kind of improved artificial immune algorithm which takes different mutation strategy toward different unit that has various quality. This algorithm conducts self-adapt adjustment between mutation rate and crossover rate in order to achieve balance between search accuracy and search efficiency. This paper conducts DAIA-BPNN short-term power load forecast model based on DAIA algorithm. It uses DAIA algorithm to optimize the weight and threshold of BPNN while overcoming the blindness when selecting the weight and threshold of BPNN. The actual calculation example of the short-term power system load forecast shows that the method presented in this paper has higher forecast accuracy and robustness compared with artificial neural networks and regression analysis model.

Published in:

2012 24th Chinese Control and Decision Conference (CCDC)

Date of Conference:

23-25 May 2012